Technical Field
This disclosure relates to additive layer 3D printing, including compensating for errors caused by shrinkage during material phase changes.
Description of Related Art
Additive manufacturing, or 3D printing, refers to a class of technologies for the direct fabrication of physical products from a 3D CAD model by a layered manufacturing process. In contrast to material removal processes in traditional machining, the 3D printing process adds material layer by layer to construct products. This technique can enable the direct printing of products with extremely complex geometry. Geometric complexity may not affect building efficiency, and so no extra effort may be necessary for molding construction or fixture tooling design. This can make 3D printing a promising manufacturing technique. See I. Gibson, D. Rosen, and B. Stucker, Additive manufacturing technologies: rapid proto-typing to direct digital manufacturing, Springer Verlag, 2009; P. Hilton and P. Jacobs, Rapid tooling: technologies and industrial applications, CRC, 2000; F. Melchels, J. Feijen, and D. Grijpma, “A review on stereolithography and its applications in biomedical engineering,” Biomaterials, vol. 31, no. 24, pp. 6121-6130, 2010; T. Campbell, C. Williams, O. Ivanova, and B. Garrett, “Could 3d printing change the world? technologies, potential, and implications of additive manufacturing,” 2011.
Despite these promising features, dimensional accuracy control can remain a bottleneck for application of 3D printing in direct manufacturing. Material solidification can be involved during layer formation, and this phase change from liquid to solid can lead to shape shrinkage and hence shape inaccuracy. See W. Wang, C. Cheah, J. Fuh, and L. Lu, “Influence of process parameters on stereolithography part shrinkage,” Materials & Design, vol. 17, no. 4, pp. 205-213, 1996. Shrinkage control may thus be crucial to overcome the barrier of dimensional accuracy in 3D printing.
To predict the final product shape in 3D printing, finite element analysis (FEA) can be employed, for example, to simulate the structural shrinkage using a linear elastic model, see G. Bugeda, M. Cervera, G. Lombera, and E. Onate, “Numerical analysis of stereolithography processes using the finite element method,” Rapid Prototyping Journal, vol. 1, no. 2, pp. 13-23, 1995), or the complete photopolymerization, mass, and heat transfer process through a comprehensive kinetic model, see Y. Tang, C. Henderson, J. Muzzy, and D. Rosen, “Stereolithography cure modeling and simulation,” International Journal of Materials and Product Technology, vol. 21, no. 4, pp. 255-272, 2004. However, the FEA method may be limited by inadequate physical understanding, and a trade-off between accuracy and computational complexity. In addition, a large number of model parameters can be difficult to acquire accurately in practice and model complexity can reduce its practicality in direct and efficient control of shape accuracy.
Empirical models have also been developed to reduce shrinkage through optimization of process parameters such as light intensity, exposure time, and layer thickness. Response surface modeling was adopted to optimize shrinkage at different directions, see J. Zhou, D. Herscovici, and C. Chen, “Parametric process optimization to improve the accuracy of rapid prototyped stereolithography parts,” International Journal of Machine Tools and Manufacture, vol. 40, no. 3, pp. 363-379, 2000, or to optimize building parameters to achieve the trade-off between accuracy, building speed, and surface finish, see C. Lynn-Charney and D. Rosen, “Usage of accuracy models in stereolithography process planning,” Rapid Prototyping Journal, vol. 6, no. 2, pp. 77-87, 2000. Designed experiments were used in S. Onuh and K. Hon, “Improving stereolithography part accuracy for industrial applications,” The International Journal of Advanced Manufacturing Technology, vol. 17, no. 1, pp. 61-68, 2001, to decrease distortion and increase flatness. However this approach may only control or reduce average shape shrinkage.
To control detailed features along the boundary of the printed product, Tong et al., K. Tong, S. Joshi, and E. Lehtihet, “Error compensation for fused deposition modeling (fdm) machine by correcting slice files,” Rapid Prototyping Journal, vol. 14, no. 1, pp. 4-14, 2008; and K. Tong, E. Lehtihet, and S. Joshi, “Parametric error modeling and software error compensation for rapid prototyping,” Rapid Prototyping Journal, vol. 9, no. 5, pp. 301-313, 2003, changed the CAD design to compensate for shrinkage, and used polynomial regression models to analyze the shrinkage in X, Y, and Z directions separately. However, prediction of deformation based on the shift of individual points can be independent of the geometry of the product, which may not be consistent with the physical manufacturing process.
To summarize, part shape deformation due to material shrinkage has long been studied, e.g., in casting and injection molding processes. Strategies and methods that have been developed to pre-scale design parts for shrinkage compensation can be classified as follows:
Machine calibration through building test parts: Similar to the calibration of CNC machines, the AM machine accuracy in x, y, z directions can be calibrated through building test cases, see K. Tong, S. Joshi, and E. Lehtihet, “Error compensation for fused deposition modeling (fdm) machine by correcting slice files,” Rapid Prototyping Journal, vol. 14, no. 1, pp. 4-14, 2008; K. Tong, E. Lehtihet, and S. Joshi, “Parametric error modeling and software error compensation for rapid prototyping,” Rapid Prototyping Journal, vol. 9, no. 5, pp. 301-313, 2003; X. Wang, “Calibration of shrinkage and beam offset in sls process,” Rapid Prototyping Journal, vol. 5, no. 3, pp. 129-133, 1999, and the dimensional accuracy of AM products is anticipated to be ensured during full production. However, the position of AM light exposure may not play the same dominant role as the tool tip position of CNC machines. As previously mentioned, part geometry and shape, process planning, materials, and processing techniques jointly can have complex effects on the profile accuracy. The calibration of the AM machine can therefore mostly be limited to the scope of a family of products, specific types of material and machine, and process planning methods.
Part geometry calibration through extensive trial-build: Besides machine calibration, another strategy is to apply either a shrinkage compensation factor uniformly to the entire product or different factors to the CAD design for each section of a product, see P. Hilton and P. Jacobs, Rapid tooling: technologies and industrial applications, CRC, 2000. However, it can be time-consuming to establish a library of compensation factors for all part shapes. The library may therefore not be inclusive. In addition, interactions between different shapes or sections may not be considered in this approach. Preliminary research shows that the strategy of applying section-wise compensation may have detrimental effects on overall shape due to “carryover effects” or interference between adjacent sections.
Simulation study based on first principles: Theoretical models for predicting shrinkage could potentially reduce experimental efforts. Models have been developed, e.g., in a powder sintering process, B. Storakers, N. Fleck, and R. McMeeking, “The viscoplastic compaction of composite powders,” Journal of the Mechanics and Physics of Solids, vol. 47, pp. 785-815, 1999; J. Secondi, “Modeling powder compaction from a pressure-density law to continuum mechanics,” Powder Metallurgy, vol. 45, no. 3, pp. 213-217, 2002, and in metal injection molding, see K. Mori, K. Osakada, and S. Takaoka, “Simplified three-dimensional simulation of non-isothermal filling in metal injection moulding by the finite element method,” Engineering Computations, vol. 13, no. 2, pp. 111-121, 1996. Although numerical FEM simulation can be developed to calculate the impact of shrinkage compensation, three-dimensional deformations and distortions in AM processes can still be rather complicated. Improving part accuracy based purely on such simulation approaches can be far from effective, and may seldom be used in practice, see D. L. Bourell, M. C. Leu, and D. W. Rosen, “Roadmap for additive manufacturing: Identifying the future of freeform processing,” Sponsored by National Science Foundation and the Office of Naval Research, Tech. Rep., 2009.
Experimental calibration strategy and first-principles-based simulation analysis both aim to control part deformation through process design. However, these strategies may fail to achieve high-precision geometric accuracy both prior to and during production. Additionally, process uncertainties may further complicate the issue of error control.